Linkedin - Data Science Foundations - Python Scientific Stack (2022)

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[ CourseBoat.com ] Linkedin - Data Science Foundations - Python Scientific Stack (2022)
  • Get Bonus Downloads Here.url (0.2 KB)
  • ~Get Your Files Here ! 01. Introduction
    • 001. The Python scientific stack.en.srt (1.4 KB)
    • 001. The Python scientific stack.mp4 (7.5 MB)
    • 002. What you should know.en.srt (0.6 KB)
    • 002. What you should know.mp4 (3.5 MB)
    • 003. Setting up.en.srt (0.6 KB)
    • 003. Setting up.mp4 (4.7 MB)
    02. Visual Studio Code
    • 004. Working with VS Code.en.srt (2.6 KB)
    • 004. Working with VS Code.mp4 (16.0 MB)
    • 005. Using code cells.en.srt (2.5 KB)
    • 005. Using code cells.mp4 (15.1 MB)
    • 006. Extensions to Python language.en.srt (2.0 KB)
    • 006. Extensions to Python language.mp4 (11.2 MB)
    • 007. Understanding markdown cells.en.srt (2.2 KB)
    • 007. Understanding markdown cells.mp4 (13.1 MB)
    03. NumPy Basics
    • 008. NumPy overview.en.srt (2.6 KB)
    • 008. NumPy overview.mp4 (14.8 MB)
    • 009. NumPy arrays.en.srt (4.7 KB)
    • 009. NumPy arrays.mp4 (27.0 MB)
    • 010. Slicing.en.srt (2.2 KB)
    • 010. Slicing.mp4 (13.3 MB)
    • 011. Boolean indexing.en.srt (4.4 KB)
    • 011. Boolean indexing.mp4 (26.5 MB)
    • 012. Understanding broadcasting.en.srt (2.9 KB)
    • 012. Understanding broadcasting.mp4 (14.8 MB)
    • 013. Understanding array operations.en.srt (1.9 KB)
    • 013. Understanding array operations.mp4 (21.5 MB)
    • 014. Understanding ufuncs.en.srt (3.7 KB)
    • 014. Understanding ufuncs.mp4 (20.4 MB)
    • 015. Challenge Working with an image.en.srt (1.7 KB)
    • 015. Challenge Working with an image.mp4 (9.8 MB)
    • 016. Solution Working with an image.en.srt (1.2 KB)
    • 016. Solution Working with an image.mp4 (7.3 MB)
    04. pandas
    • 017. pandas overview.en.srt (2.1 KB)
    • 017. pandas overview.mp4 (11.6 MB)
    • 018. Loading CSV files.en.srt (4.7 KB)
    • 018. Loading CSV files.mp4 (25.3 MB)
    • 019. Parsing time.en.srt (2.6 KB)
    • 019. Parsing time.mp4 (15.5 MB)
    • 020. Accessing rows and columns.en.srt (6.0 KB)
    • 020. Accessing rows and columns.mp4 (33.0 MB)
    • 021. Calculating speed.en.srt (5.4 KB)
    • 021. Calculating speed.mp4 (27.7 MB)
    • 022. Displaying speed box plot.en.srt (4.1 KB)
    • 022. Displaying speed box plot.mp4 (22.0 MB)
    • 023. Challenge Taxi data mean speed.en.srt (0.5 KB)
    • 023. Challenge Taxi data mean speed.mp4 (3.3 MB)
    • 024. Solution Taxi data mean speed.en.srt (1.2 KB)
    • 024. Solution Taxi data mean speed.mp4 (7.5 MB)
    05. Installing Packages
    • 025. Introduction to Python packages.en.srt (4.1 KB)
    • 025. Introduction to Python packages.mp4 (24.2 MB)
    • 026. Using environments.en.srt (3.2 KB)
    • 026. Using environments.mp4 (19.3 MB)
    • 027. Managing dependencies.en.srt (2.4 KB)
    • 027. Managing dependencies.mp4 (12.8 MB)
    • 028. Challenge Creating requirements.en.srt (0.5 KB)
    • 028. Challenge Creating requirements.mp4 (3.1 MB)
    • 029. Solution Creating requirements.en.srt (0.4 KB)
    • 029. Solution Creating requirements.mp4 (2.9 MB)
    06. folium and Geo
    • 030. Creating an initial map.en.srt (2.3 KB)
    • 030. Creating an initial map.mp4 (13.3 MB)
    • 031. Drawing a track on a map.en.srt (4.1 KB)
    • 031. Drawing a track on a map.mp4 (23.6 MB)
    • 032. Using geo data with Shapely.en.srt (5.9 KB)
    • 032. Using geo data with Shapely.mp4 (34.5 MB)
    • 033. Challenge Drawing the running track.en.srt (0.4 KB)
    • 033. Challenge Drawing the running track.mp4 (2.3 MB)
    • 034. Solution Drawing the running track.en.srt (1.1 KB)
    • 034. Solution Drawing the running track.mp4 (7.3 MB)
    07. NYC Taxi Data
    • 035. Examining data.en.srt (2.5 KB)
    • 035. Examining data.mp4 (13.3 MB)
    • 036. Loading data from CSV files.en.srt (2.3 KB)
    • 036. Loading data from CSV files.mp4 (13.3 MB)
    • 037. Working with categorical data.en.srt (3.9 KB)
    • 037. Working with categorical data.mp4 (21.6 MB)
    • 038. Working with data Hourly trip rides.en.srt (6.1 KB)
    • 038. Working with data Hourly trip rides.mp4 (34.7 MB)
    • 039. Working with data Rides per hour.en.srt (5.0 KB)
    • 039. Working with data Rides per hour.mp4 (28.4 MB)
    • 040. Working with data Weather data.en.srt (5.0 KB)
    • 040. Working with data Weather data.mp4 (26.7 MB)
    • 041. Challenge Graphing taxi data.en.srt (0.9 KB)
    • 041. Challenge Graphing taxi data.mp4 (5.6 MB)
    • 042. Solution Graphing taxi data.en.srt (3.8 KB)
    • 042. Solution Graphing taxi data.mp4 (21.7 MB)
    08. scikit-learn
    • 043. scikit-learn introduction.en.srt (1.9 KB)
    • 043. scikit-learn introduction.mp4 (11.1 MB)
    • 044. Regression.en.srt (8.5 KB)
    • 044. Regression.mp4 (46.2 MB)
    • 045. Understanding train test split.en.srt (2.9 KB)
    • 045. Understanding train test split.mp4 (17.3 MB)
    • 046. Preprocessing data.en.srt (4.4 KB)
    • 046. Preprocessing data.mp4 (25.8 MB)
    • 047. Composing pipelines.en.srt (2.8 KB)
    • 047. Composing pipelines.mp4 (15.3 MB)
    • 048. Saving and loading models.en.srt (2.5 KB)
    • 048. Saving and loading models.mp4 (14.1 MB)
    • 049. Challenge Hand-written digits.en.srt (1.8 KB)
    • 049. Challenge Hand-written digits.mp4 (10.7 MB)
    • 050. Solution Hand-written digits.en.srt (1.8 KB)
    • 050. Solution Hand-written digits.mp4 (10.3 MB)
    09. Plotting
    • 051. Matplotlib overview.en.srt (1.5 KB)
    • 051. Matplotlib overview.mp4 (8.9 MB)
    • 052. Using styles.en.srt (2.2 KB)
    • 052. Using styles.mp4 (13.3 MB)
    • 053. Customizin

Description

Data Science Foundations: Python Scientific Stack (2022)



https://CourseBoat.com

LinkedIn Learning
Duration: 2h 21m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 1.21 GB
Genre: eLearning | Language: English

Data science provides organizations with striking—and extremely valuable—insights into human behavior. While data mining can seem a bit daunting, you don't need to be a highly skilled programmer to process your own data. In this new hands-on course, instructor Miki Tebeka shows you how to use the Python scientific stack to complete common data science tasks.

Learn about the basic tools and core concepts to effectively process data with the Python scientific stack, including how to work with Visual Studio Code for documentation, NumPy for numeric computation, Pandas for data crunching, scikit-learn for data modeling, madplotlib for data visualization, and more. Upon completing this course, you’ll have the skills you need to load and analyze data, run models, solve complex problems, and display results for your team.



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Linkedin - Data Science Foundations - Python Scientific Stack (2022)


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Linkedin - Data Science Foundations - Python Scientific Stack (2022)


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